\begin{table}[!t]

\caption{\label{tab:classifier_results}Out-of-sample predictive performance of ``best'' classifiers trained with different text representation and learning strategies. The XLM-T model \citep{barbieri_xlm-t_2021} fine-tuned on our training data outperforms a l1-regularized linear model (``ridge'' regression) trained with tweets' multilingual sentence embeddings (MSEs) as inputs as well as a ridge regression trained using bag-of-words representations of machine-translated tweet texts (MT+BoW).}
\centering
\fontsize{9}{11}\selectfont
\begin{tabular}[t]{lrrrrr}
\toprule
\multicolumn{1}{c}{\em{ }} & \multicolumn{2}{c}{\em{Overall}} & \multicolumn{3}{c}{\em{General elite criticism class}} \\
\cmidrule(l{3pt}r{3pt}){2-3} \cmidrule(l{3pt}r{3pt}){4-6}
Model & $F1_{\mbox{macro}}$ & $F1_{\mbox{micro}}$ & Precision & Recall & Specificity\\
\midrule
\textbf{XLM-T classifier} & \textbf{0.745} & \textbf{0.815} & \textbf{0.644} & \textbf{0.581} & \textbf{0.893}\\
MSEs (w/ linear classifier) & 0.685 & 0.737 & 0.482 & 0.660 & 0.763\\
MT+BoW (w/ linear classifier) & 0.611 & 0.674 & 0.391 & 0.543 & 0.718\\
\bottomrule
\end{tabular}
\end{table}
